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The main task was to make predictions for future sales based on time-series analysis, which is found in Part 2.  I identified overarching trends, found seasonality, and tested for cyclic contributions.  I then looked at residuals and used this information, and numerous visuals, to predict an additional week's worth of data.\n\n## Results\nI was able to identify a slight trend and some seasonality to the data as well.  Unforutnatley, once removed, a lot of \"noise\" was left-- there were no cycles I could identify from using lag features, and the residuals were randomly distributed.  This expected, for the most part, with sales data-- we see some seasonality with quarters and holidays, depending on what is being sold, but there can be any number of reasons why there are spikes or lulls.  \n\n## Screenshots\n![screenshot3](https://github.com/dmarks84/Ind_Project_Superstore-Sales-Time-Series-Analysis--Kaggle/blob/main/superstore_screenshot3.png?raw=true)\n![screenshot2](https://github.com/dmarks84/Ind_Project_Superstore-Sales-Time-Series-Analysis--Kaggle/blob/main/superstore_screenshot2.png?raw=true)\n![screenshot1](https://github.com/dmarks84/Ind_Project_Superstore-Sales-Time-Series-Analysis--Kaggle/blob/main/superstore_screenshot1.png?raw=true)\n\n## Skills (Developed \u0026 Applied)\nProgramming, Python, Statistics, Numpy, Pandas, Matplotlib, Scikit-learn, Dataframes, Data Modeling, EDA, Data Visualization, Data Reporting, Time-Series Analysis, Seaborn, Supervised ML, StatsModels\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Find_project_superstore-sales-time-series-analysis--kaggle","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdmarks84%2Find_project_superstore-sales-time-series-analysis--kaggle","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdmarks84%2Find_project_superstore-sales-time-series-analysis--kaggle/lists"}